# Importing libraries - Gets us the software packages we need to do stuff import pandas as pd from matplotlib import pyplot as plt import seaborn as sns import numpy as np %matplotlib inline from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.ensemble import RandomForestClassifier

# Loading our data and storing it in a variable so that we can refer to it later cancer_df = pd.read_csv("data.csv")

# Looking at the first few rows of our data cancer_df.head(15)

cancer_df['diagnosis'].value_counts().plot(kind = "bar") cancer_df['diagnosis'].value_counts()

cancer_df['diagnosis'] = cancer_df['diagnosis'].map({'B' : 0, 'M': 1})

cancer_df = cancer_df.filter(regex='.*_mean|diagnosis', axis=1) cancer_df.drop(columns = 'id', inplace=True, errors='ignore') cancer_df

cancer_df.describe()

## You DO NOT HAVE TO understand this code df = cancer_df.copy() df.head() features_mean=list(df.columns[1:11]) # split dataframe into two based on diagnosis dfM=df[df['diagnosis'] ==1] dfB=df[df['diagnosis'] ==0] #Stack the data plt.rcParams.update({'font.size': 8}) fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(8,10)) axes = axes.ravel() for idx,ax in enumerate(axes): ax.figure binwidth= (max(df[features_mean[idx]]) - min(df[features_mean[idx]]))/50 ax.hist([dfM[features_mean[idx]],dfB[features_mean[idx]]], bins=np.arange(min(df[features_mean[idx]]), max(df[features_mean[idx]]) + binwidth, binwidth) , alpha=0.5,stacked=True, density = True, label=['M','B'],color=['r','g']) ax.legend(loc='upper right') ax.set_title(features_mean[idx]) plt.tight_layout() plt.show()

training_data, test_data = train_test_split(cancer_df, test_size=0.3, random_state=2) training_data.head() # common split is 80% training, 20% testing but you may have to adjust based off of dataset size

test_data.columns

X_train = training_data.drop(columns='diagnosis')[['radius_mean', 'perimeter_mean', 'area_mean', 'compactness_mean', 'concave points_mean']] y_train = training_data['diagnosis'] X_test = test_data.drop(columns='diagnosis')[['radius_mean', 'perimeter_mean', 'area_mean', 'compactness_mean', 'concave points_mean']] y_test = test_data['diagnosis']

logistic_model = LogisticRegression() logistic_model.fit(X_train, y_train)

y_predicted_logistic = logistic_model.predict(X_test) accuracy = metrics.accuracy_score(y_predicted_logistic,y_test) print("Accuracy : %s" % "{0:.3%}".format(accuracy))

pd.concat([pd.Series(y_predicted_logistic).reset_index(), pd.Series(y_test).reset_index()], axis = 1)[[0,'diagnosis']].rename(columns={0:'predicted','diagnosis':'actual'})

from sklearn import metrics from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_predicted_logistic) sns.set(font_scale=3) fig, ax = plt.subplots(figsize=(10, 10)) sns.heatmap(cm, annot=True, fmt='g', ax=ax); #annot=True to annotate cells, ftm='g' to disable scientific notation # labels, title and ticks ax.set_xlabel('Predicted labels', fontsize = 18); ax.set_ylabel('True labels', fontsize = 18); ax.set_title('Confusion Matrix', fontsize = 22); ax.xaxis.set_ticklabels(['benign', 'malignant'], fontsize = 14); ax.yaxis.set_ticklabels(['benign', 'malignant'], fontsize = 14); cm